Based on human vision system, color can be described by brightness, hue and saturation. Usually, hue and saturation are generally referred to as chroma, which is used to represent the category and depth of color. In the video encoding process, for different frames, regions people cared about are dynamically changed, which requires that the algorithm is able to adjust transformation function according to the change of the video sequences, so that brightness distribution of the image can be improved according to demand in various scenes. The visual quality of the image can be improved by a constant transformation function of brightness with the parameters obtained by considerable statistical experiments. However, if the same approach used in the ordinary scenes is carried out in some specific scenes (such as a wholly dark scene), visual quality of the image will be decreased.
For color information of an object, people always hope that, the more colorful the better. Considering the requirement of visual comfort, the bigger the transform intensity is, the more color of the image with insufficient chroma information is improved. Skin color of human beings is between yellow and red. If the same model is used for the whole region, taking relatively large adjusting values, uncomfortable feeling to skin color will be generated, and taking relatively small adjusting values, the requirement of enhancing color information of objects in other color gamut will be restricted. If the algorithm is dependent on the detection of skin color regions, firstly, computational complexity is increased, and secondly there isn't a detection algorithm for skin color regions with 100% accuracy, thirdly many problems such as balance transition brought in by incorrect judgment of discrete point field will occur. Although people are more sensitive to luminance than to chrominance, preprocessing should be employed to enhance the color of the image, since chroma information carried by the image sequence (such as image captured by a camera) processed by the video encoder is insufficient at some time. Most conventional color processing methods are based on RGB or HSV color model, while a separate representation mode of luminance and chrominance, i.e., YUV, is used in video encoding. Although transformation between different models can be realized through color space transformation technology, computational complexity bought in by transformation and invert transformation is also considerable.
Image quality will be decreased in varying degrees after encoding. Problems, such as blocking artifacts brought in by block-based encoding and decoding strategy, attenuation and losing of high frequency information and so on, are present in the image sequence after decoding. In order to eliminate blocking artifacts without losing of boundary high frequency information, and take characteristics of block-based encoding and decoding strategy into account that the blocking artifacts always present at the boundary between blocks, a method for block-based boundary adaptive enhancement is employed.